For over three decades numerous studies have tried to understand the processes and impacts of air-sea interactions on the atmosphere and oceans, particularly in predicting winds and waves under tropical cyclone conditions. Literature has highlighted the critical role of momentum transfer, with various parameterisations proposed for the momentum fluxes, through the drag coefficient (Cd) and the roughness (z0). However, accurate predictions still remain a significant challenge. Recently, Du et al. (2017,2019) proposed a comprehensive calculation of the source input function using a Wave Boundary Layer Model (WBLM). However, their study used a standalone model rather than a coupled system. Given the established significance of two-way wind-wave systems (Janssen, 1991), this study implements the WBLM within a coupled model (OpenIFS), to evaluate its impact and discuss the potential and limitations of the method. Numerical simulations were conducted using the WBLM scheme for a selected tropical cyclone, with results compared against in-situ buoy measurements and satellite altimeter data. Furthermore, the new approach’s results were compared with outputs using the default, well-established source input function of OpenIFS (Janssen et al., 1989; Janssen, 1991) to further assess its effectiveness. The findings suggest that the WBLM tends to reduce the commonly overestimated drag and Charnock coefficients. However, comparisons with in-situ observations indicate that the new approach requires substantial refinements to improve wind and wave predictions, since there are cases that the WBLM scheme under-performs the default scheme. This discrepancy may be attributed to the calculation of high-frequency impacts on momentum exchanges.

Sea level rise and consequent coastal flooding pose an increasing threat to socioeconomic stability globally, particularly in China's coastal zone (CCZ). However, the long-term spatial correlations between coastal flood hazards (CFH) and socioeconomic exposure (SEE) remain unclear, thereby limiting the accuracy of coastal flood risk assessments. Consequently, novel frameworks are necessary for effective risk management and adaptation strategies. This study employs the Bivariate Local Moran's I approach to analyze the spatiotemporal intersection patterns between CFH and SEE from 2030 to 2100 in the CCZ under various Representative Concentration Pathway-Shared Socioeconomic Pathway (RCP-SSP) scenarios. CFH is evaluated using a depth-affected function, while SEE is quantified through the analytic hierarchy process-entropy weighting (AHP-EW) method. The results indicate that the areas of "High-High" clusters (regions where higher CFH levels are surrounded by higher SEE levels) will increase to 10.26 × 103 km2, 11.86 × 103 km2, and 14.33 × 103 km2 by 2100 under the RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP5 scenarios, respectively. These clusters are primarily located in Guangdong, Jiangsu, and Zhejiang, where immediate and robust interventions are essential to protect human and economic assets. By contrast, the areas of "High-Low" clusters (regions where higher CFH levels are surrounded by lower SEE levels) will expand to 17.78 × 103 km2, 16.15 × 103 km2, and 17.31 × 103 km2 by 2100 under the RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP5 scenarios, respectively. These clusters are mainly located in Jiangsu, Shandong, and Shanghai, where long-term preventive risk management measures are recommended. By 2100, the GDP and population in the "High-High" clusters are projected to reach $2675.32 billion (13.17 million people), respectively, under the RCP8.5-SSP5 scenario, representing the most severe threat to both economic and population stability. These findings underscore the importance of applying a differentiated risk management approach when formulating coastal flood adaptation policies, as well as providing valuable insights for enhancing the resilience of vulnerable areas in the CCZ to climate change.

Coastal flooding due to sea level rise significantly affects socioeconomic development. The dynamic nature of coastal flood risk (CFR) and socioeconomic development level (SDL) leads to uncertainties in understanding their future interplay. This ambiguity challenges coastal nations in devising effective flood adaptation and coastal management strategies. This study quantitatively examines the expected GDP affected (EGA) and population affected (EPA) by coastal flooding in China's coastal zone (CCZ) from 2030 to 2100 under various climate scenarios (RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP5). The future SDL in CCZ is assessed using a method combining the analytic hierarchy process with entropy weight. The future CFR-SDL dynamic relationship is analyzed using the coupling coordination degree (CCD) model. The results reveal that in CCZ under the RCP2.6SSP1, RCP4.5-SSP2, and RCP8.5-SSP5 scenarios: by 2100, the EGA and EPA will reach $814.90 billion & 6.17 million people, $828.16 billion & 7.63 million people, and $1568.83 billion & 8.05 million people, respectively, where the coastal cities in Jiangsu and Guangdong provinces will face more obvious risks of socioeconomic losses; The total area in the CCZ at "Very high" and "High" level of socioeconomic development by 2100 is projected to reach 11.33 x 103 km2, 12.86 x 103 km2, and 15.82 x 103 km2, respectively, with the Pearl River Delta, Yangtze River Delta, and Tianjin-Hebei remaining pivotal for CCZ's socioeconomic growth. Cities such as Lianyungang, Jiaxing, Shenzhen, Dongguan, and Foshan show notable CCD characteristics, and addressing the trade-off between SDL and CFR is crucial in achieving sustainable development. This study highlights the potential socioeconomic impacts of coastal flooding and emphasizes the importance of considering the interrelationship between CFR and SDL when developing coastal flood adaptation policies.

Abstract. Land surface models such as the Joint UK Land Environment Simulator (JULES) are increasingly used for hydrological assessments because of their state-of-the-art representation of physical processes and versatility. Unlike statistical models and AI models, the JULES model simulates the physical water flux under given meteorological conditions, allowing us to understand and investigate the cause and effect of environmental processes changes. Here we explore the possibility of this approach using a case study in the Atibaia river basin, which serves as a major water supply for metropolitan regions of Campinas and São Paulo, Brazil. The watershed is suffering increasing hydrological risks, which could be attributed to environmental changes, such as urbanization and agricultural activity. The increasing risks highlight the importance to evaluate the land surface processes of the watershed systematically. We explore the use of local precipitation collection complement with multiple sources of global reanalysis data to simulate the basin hydrology. Our results show that the coarse resolution of rainfall data is the main reason to reduce model performance. Despite this shortcoming, key hydrological fluxes in the basin can be represented by the JULES model simulations.

Despite of significant improvement in modelling of the atmosphere after years of research, the accuracy of predicting cyclone/typhoon waves still remains highly challenging. Evidence shows that the air-sea-waves interaction over the ocean surface can significantly impact on the coupled atmosphere-ocean systems, through momentum, mass, and energy exchanges. In particular, the momentum exchanges have been found to affect both the structure of the wave boundary layer and the sea state, through the wave dissipation and wave breaking. For many decades, studies suggested different parameterizations of the momentum fluxes, through drag coefficient (Cd) and the roughness length (z0). In recent years, research has been focused on the theoretical approaches of the momentum parameterization within the Wave Boundary Layer (WBL) in order to obtain the best Cd and z0 (Hara and Belcher 2002,2004; Moon et al. 2004; Du et al. 2017,2019). In this study, based on the works of Du et al. (2017, 2019), we introduce a new approach of the parameterization of the momentum flux using the roughness length. The potential of the scheme is analysed with extreme wind and wave events and the results are validated against buoy observations.

For more than 30 years, many studies have been carried out to improve the understanding of the air-sea interaction and its impact on the predictions of atmospheric and the oceanic processes. It is well understood that the accuracy in predictions of the wind-driven waves is highly dependent on the source input and dissipation terms. The Wave Boundary Layer (WBL) approach for the estimation of surface stress has previously been used to improve the wind and wave simulations under extreme conditions. However, until recently the WBL was only used to determine the roughness length (z0) and drag coefficient (Cd), but not to alter the wind input source function in wave models. In this study, the wave boundary layer model (WBLM) was implemented in the OpenIFS coupled model as source functions as suggested by Du et al. (2017, 2019). The new wind input and dissipation terms are then tested using numerical model simulations, with a particular focus on the contribution of the high frequency tail in the source input function. The comparison of the results of this study with published results hints at better performance of the model on the estimation of the roughness length and drag coefficient. This should improve predictions of the significant wave height and wind speeds, especially under extreme conditions. Corresponding Author: Nefeli Makrygianni (makrygiannin@cardiff.ac.uk)

Accurate estimation of evapotranspiration is generally constrained due to lack of required hydrometeorological datasets. This study addresses the performance analysis of reference evapotranspiration (ETo) estimated from NASA/POWER, National Center for Environmental Prediction (NCEP) global reanalysis data before and after dynamical downscaling through the Weather Research and Forecasting (WRF) model. The state-of-the-art Hamon's and Penman-Monteith's methods were utilized for the ETo estimation in the Northern India. The performance indices such as bias, root mean square error (RMSE), and correlation (r) were calculated, which showed the values 0.242, 0.422, and 0.959 for NCEP data (without downscaling) and 0.230, 0.402, and 0.969 for the downscaled data respectively. The results indicated that after WRF downscaling, there was some marginal improvement found in the ETo as compared to the without downscaling datasets. However, a better performance was found in the case of NASA/POWER datasets with bias, RMSE, and correlation values of 0.154, 0.348, and 0.960 respectively. In overall, the results indicated that the NASA/POWER and WRF downscaled data can be used for ETo estimation, especially in the ungauged areas. However, NASA/POWER is recommended as the ETo calculations are less computationally expensive and easily available than performing WRF simulations.

A crucial part in designing a robust water quality monitoring network is the selection of appropriate water quality sampling locations. Due to cost and time constraints, it is essential to identify and select these locations in an accurate and efficient manner. The main contribution of the present article is the development of a practical methodology for allocating critical sampling points in present and future conditions of the non-point sources under a case study of the Khoy watershed in northwest Iran, where financial resources and water quality data are limited. To achieve this purpose, the river mixing length method (RML) was applied to propose potential sampling points. A new non-point source potential pollution score (NPPS) was then proposed by the analytic network process (ANP) to classify the importance of each sampling point prior to selecting the most appropriate locations for a river system. In addition, an integrated cellular automata–Markov chain model (CA–Markov) was applied to simulate future change in non-point sources during the period 2026–2036. Finally, by considering anthropogenic activities through land-use mapping, the hierarchy value, the non-point source potential pollution score values and budget deficiency in the study area, the seven sampling points were identified for the present and the future. It is not expected, however, that the present location of the proposed sampling points will change in the future due to the forthcoming changes in non-point sources. The current study provides important insights into the design of a reliable water quality monitoring network with a high level of assurance under certain changes in non-point sources. Furthermore, the results of this study should be valuable for water quality monitoring agencies looking for a cost-effective approach for selecting sampling locations.

This study has proposed and investigated a novel input variable selection method for nonlinear modelling based on principle component analysis (PCA) and cluster analysis. The proposed approach was applied to daily rainfall-runoff modelling of the Brue catchment of the United Kingdom using wavelet based hybrid forms of two nonlinear models, Artificial Neural Networks (ANNs) and Local Linear Regression (LLR), to identify meaningful wavelet decomposed sub-series. The homogenous group formation capability of cluster analysis and redundancy assessment capability of PCA were applied effectively in this study to solve input selection uncertainties associated with wavelet based hybrid models. Though this concept has been represented in the selection of effective wavelet decomposed subseries in runoff modelling, the application has gotten wider implications in time series modelling with highly redundant and large input space. The study revealed the weakness of conventional forms of cross-correlation analysis and also suggested that input selection could be improved by making sufficient natural clusters (equal to the desired number of input data series) of input space and restricting the search within each cluster according to silhouette or correlation value. The study also highlighted the higher modelling capability of ANN over traditional LLR models in rainfall-runoff modelling of the Brue catchment.

Surat city of India, situated 100 km downstream of Ukai Dam and 19.4 km upstream from the mouth of River Tapi, has experienced the largest flood in 2006. The peak discharge of about 25,770 m 3 s −1 released from the Ukai Dam was responsible for a disaster. To assess the flood and find inundation in low-lying areas, simulation work is carried out under the 1D/2D couple hydrodynamic modeling. Two hundred ninety-nine cross sections, two hydraulic structures and five major bridges across the river are considered for 1D modeling, whereas a topographic map at 0.5 m contour interval was used to produce a 5 m grid, and SRTM (30 and 90 m) grid has been considered for Surat and the Lower Tapi Basin. The tidal level at the river mouth and the release from the Ukai Dam during 2006 flood are considered as the downstream and upstream boundaries, respectively. The model is simulated under the unsteady flow condition and validated for the year 2006. The simulated result shows that 9th August was the worst day in terms of flooding for Surat city and a maximum 75–77% area was under inundation. Out of seven zones, the west zone had the deepest flood and inundated under 4–5 m. Furthermore, inundation is simulated under the bank protection work (i.e., levees, retaining wall) constructed after the 2006 flood. The simulated results show that the major zones are safe against the inundation under 14,430 m 3 s −1 water releases from Ukai Dam except for the west zone. The study shows the 2D capability of new HEC-RAS 5 for flood inundation mapping and management studies.

A remarkable decline in the number of rain gauges is being faced in many areas of the world, as a compromise to the expensive cost of operating and maintaining rain gauges. The question of how to effectively deploy new or remove current rain gauges in order to create optimal rainfall information is becoming more and more important. On the other hand, larger-scaled, remotely sensed rainfall measurements, although poorer quality compared with traditional rain gauge rainfall measurements, provide an insight into the local storm characteristics, which are sought by traditional methods for designing a rain gauge network. Based on these facts, this study proposes a new methodology for rain gauge network design using remotely sensed rainfall datasets that aims to explore how many gauges are essential and where they should be placed. Principal component analysis (PCA) is used to analyze the redundancy of the radar grid network and to determine the number of rain gauges while the potential locations are determined by cluster analysis (CA) selection. The proposed methodology has been performed on 373 different storm events measured by a weather radar grid network and compared against an existing dense rain gauge network in southwestern England. Because of the simple structure, the proposed scheme could be easily implemented in other study areas. This study provides a new insight into rain gauge network design that is also a preliminary attempt to use remotely sensed data to solve the traditional rain gauge problems.

(1) School of Technology,Pandit Deendayal Petroleum University, Gujarat, India (dhruvesh.patel@pdpu.ac.in), (2) University of Bern, Institute of Geography, Bern, Switzerland, (3) Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India, (4) Hydro-environmental Research Centre, School of Engineering, Cardiff University, Cardiff, UK, (5) Department of Civil Engineering, University of Bristol, Bristol, UK

It is essential to monitor urban evolution at spatial and temporal scales to improve our understanding of the changes in cities and their impact on natural resources and environmental systems. Various aspects of remote sensing are routinely used to detect and map features and changes on land and sea surfaces, and in the atmosphere that affect urban sustainability. We provide a critical and comprehensive review of the characteristics of remote sensing systems, and in particular the trade-offs between various system parameters, as well as their use in two key research areas: (a) issues resulting from the expansion of urban environments, and (b) sustainable urban development. The analysis identifies three key trends in the existing literature: (a) the integration of heterogeneous remote sensing data, primarily for investigating or modelling urban environments as a complex system, (b) the development of new algorithms for effective extraction of urban features, and (c) the improvement in the accuracy of traditional spectral-based classification algorithms for addressing the spectral heterogeneity within urban areas. Growing interests in renewable energy have also resulted in the increased use of remote sensing—for planning, operation, and maintenance of energy infrastructures, in particular the ones with spatial variability, such as solar, wind, and geothermal energy. The proliferation of sustainability thinking in all facets of urban development and management also acts as a catalyst for the increased use of, and advances in, remote sensing for urban applications.

Climate change is expected to result in an increase in the frequency and intensity of extreme weather events. In turn, this will result in more frequent occurrences of extreme flood events, such as flash flooding and large-scale river flooding. This being the case, there is a need for more accurate flood risk assessment schemes, particularly in areas prone to extreme flooding. This study investigates what type of flood hazard assessment methods should be used for assessing the flood hazard to people caused by extreme flooding. Two flood hazard assessment criteria were tested, namely: a widely used, empirically derived method, and recently introduced, physically based and experimentally calibrated method. The two selected flood hazard assessment methods were: (1) validated against experimental data, and (2) used to assess flood hazard indices for two different extreme flood events, namely: the 2010 Kostanjevica na Krki extreme river flood and the 2007 Železniki flash flood. The results obtained in this study suggest that in the areas prone to extreme flooding, the flood hazard indices should be based on using the formulae derived for a mechanics-based analysis, as these formulations consider all of the physical forces acting on a human body in floodwaters, take into account the rapid changes in the flow regime, which often occur for extreme flood events, and enable a rapid assessment of the degree of flood hazard risk in a short time period, a feature particularly important when assessing flood hazard indices for high Froude numbers flows.